A control device includes: a control unit that performs control to make a pedestrian crossing appear in an area from a first side to a second side of a road when a pedestrian crosses the road, and that detects at least one vehicle passing through the road; and a communication unit that transmits instruction data instructing the at least one vehicle detected by the control unit to avoid a position that hinders crossing of the pedestrian.

A control device includes: a control unit that performs control to make a pedestrian crossing appear in an area from a first side to a second side of a road when a pedestrian crosses the road, and that detects at least one vehicle passing through the road; and a communication unit that transmits instruction data instructing the at least one vehicle detected by the control unit to avoid a position that hinders crossing of the pedestrian.

A traffic intersection monitoring system and method to ensure a traffic signal is viewable by vehicles, bicyclists, and pedestrians, by measuring the color light intensity of the signal light, and monitoring the orientation of the traffic signal light. Each phase of color is timed and coordinated with traffic counters to generate a real-time status of predicted future traffic signal light color with corresponding traffic volume. The predicted status and traffic counts are published for the use of any applicable subscriber of the traffic intersection monitoring system.

A traffic intersection monitoring system and method to ensure a traffic signal is viewable by vehicles, bicyclists, and pedestrians, by measuring the color light intensity of the signal light, and monitoring the orientation of the traffic signal light. Each phase of color is timed and coordinated with traffic counters to generate a real-time status of predicted future traffic signal light color with corresponding traffic volume. The predicted status and traffic counts are published for the use of any applicable subscriber of the traffic intersection monitoring system.

Systems, apparatus and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may include a light emitter positioned external to a surface of the autonomous vehicle and being configured to implement a visual alert by emitting light from the light emitter. Data representing a light pattern may be received by the light emitter and the light emitted by the display may be indicative of the light pattern. The light pattern may be selected to gain the attention of the object (e.g., a pedestrian, a driver of a car, a bicyclists, etc.) in order to avoid the potential collision or to alert the object to the presence of the autonomous vehicle.

Systems, apparatus and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may include a light emitter positioned external to a surface of the autonomous vehicle and being configured to implement a visual alert by emitting light from the light emitter. Data representing a light pattern may be received by the light emitter and the light emitted by the display may be indicative of the light pattern. The light pattern may be selected to gain the attention of the object (e.g., a pedestrian, a driver of a car, a bicyclists, etc.) in order to avoid the potential collision or to alert the object to the presence of the autonomous vehicle.

A method is described for creating observation data, in particular by at least one vehicle, traveled road segments being ascertained by the vehicle, lanes of the road segments traveled by the vehicle being ascertained by the vehicle, and the ascertained road segments together with the ascertained traveled lanes being transmitted as observation data from the vehicle to an external server unit. A method for ascertaining a number of traffic lanes, to a system, to an external server unit, and to a control unit are also described.

A method is described for creating observation data, in particular by at least one vehicle, traveled road segments being ascertained by the vehicle, lanes of the road segments traveled by the vehicle being ascertained by the vehicle, and the ascertained road segments together with the ascertained traveled lanes being transmitted as observation data from the vehicle to an external server unit. A method for ascertaining a number of traffic lanes, to a system, to an external server unit, and to a control unit are also described.

A method for predicting traffic light information by using a LIDAR is provided. The method includes steps of: (a) on condition that each of metadata has been allocated for each of virtual boxes included in a region covered by the LIDAR, obtaining, by a server, at least part of start timing information and stop timing information of a plurality of vehicles for each of the virtual boxes; and (b) predicting, by the server, each of pieces of the traffic light information respectively corresponding to each of the virtual boxes by referring to at least part of the start timing information and the stop timing information of the vehicles for each of the virtual boxes.

A system for identifying redundant road lane detections in map data and subsequently updating the map data to remove the redundant road lane detections is provided. The system may be configured to determine, based on sensor data, a plurality of road lane detections associated with a road link represented by the map data. The system is further configured to determine a cluster for the road link based on a clustering criterion. The system is further configured to establish a plurality of road lane detection groups based on connectivity of the road lane detections in the cluster. The plurality of road lane detection groups is evaluated to identify one or more redundant road lane detections based on one or more of a parallel-detection criterion or a heading difference criterion. The identified redundant road lane detections are used to update the map data by a computer-implemented update process.